Examination arrangement

Course content

This course covers analysis of variance (ANOVA), correlation analysis and simple and multiple linear and logistic regression analysis. Regression models are used to examine associations between a single outcome (dependent) variable and one or more explanatory (independent) variables. A linear regression model is applied when the outcome variable is a continuous variable, whereas a logistic regression model can be applied when the outcome variable is a binary categorical variable. ANOVA is used for comparing mean values between three or more groups. The ANOVA model can be rewritten into a general linear model and can then be extended to include adjustment for categorical as well as continuous variables (analysis of covariance, ANCOVA). The theoretical background for the methods will be given, but the main focus will be on how to apply the methods in medical research. The course covers model specification (including how to use interaction terms to allow for subgroup-specific effects), estimation of model parameters, evaluation of model assumptions and how to deal with deviation from assumptions, and interpretation and presentation of results. The course also covers evaluation of model fit and a brief discussion of variable selection. In addition, some non-parametric methods will be presented. An important part of the course is to perform data analyses by means of statistical software. Examples from scientific papers will be given.

Learning outcome

Knowledge

After successful completion of this course the student should

• have gained sufficient theoretical knowledge of the statistical methods covered by the course to be able to correctly apply the methods in a medical research project on PhD-level

Skills

After successful completion of this course the student should be able to

• select the most appropriate statistical method and model based on the research question, design of study and nature of the data
• independently perform a statistical analysis by the means of a statistical software package
• evaluate the assumptions made on the applied model or method
• interpret and critically evaluate the results from the statistical analysis
• present the results from the statistical analysis in a format applicable for publication in a scientific medical journal

General competence

After successful completion of this course the student should

• be able to evaluate and discuss application of the statistical methods covered by this course in medical research projects

Learning methods and activities

Lectures and exercises. Data analyses by means of statistical software.

Recommended previous knowledge

Introductory course in medical statistics (KLMED8004 or equivalent). The students need to be familiar with basic concepts of statistics (continuous and categorical variables, probability and probability distributions), as well as general principles of statistical analysis (estimation and hypothesis testing, statistical inference). Prior training in use of statistical software is recommended.

Course materials

Textbook by Rosner, B: "Fundamentals of Biostatistics", Cengage Learning, 8th ed. 2016.

Recommended supplementary text book:

Hosmer, D.W., Lemeshow, S. and Sturdivant, R.X.: Applied logistic regression, Wiley Series in Probability and Statistics, 3rd ed. 2013

Learning materials handed out during the course.

Learning materials/text book may be changed.

Credit reductions